geospatial information utility: an estimation of the...

www.elsevier.com/locate/dsw

s 38 (2004) 47–63
Decision Support System
0167-9

doi:10.

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378-54

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dasgup

Geospatial information utility: an estimation of the relevance

of geospatial information to users

W. Lee Meeksa,*, Subhasish Dasguptab

aGeospatial Intelligence Department, Veridian Systems Division, Veridian Corporation, Chantilly, VA, USAbManagement Science Department, MON 403, The George Washington University, 2115 G Street, NW, Washington, DC 20052, USA

Received 1 September 2001; accepted 1 March 2003

Available online 25 July 2003

Abstract

As the acquisition and use of information are costly, the optimal use of information involves economic tradeoffs. Therefore,

valuing information is attracting research and thought. However, till now, little attention has been paid to the geospatial

information domain, which is increasingly coming to the attention of decision makers seeking to improve decision models by

considering spatio-temporal factors. This paper proposes a metric called Geospatial Information Utility (GeoIU), which will

allow decision makers to assess the degree of utility of accessed geospatial data sets when making decisions that incorporate

those geospatial data and information. The GeoIU metric uses multi-attribute utility theory to assess, score, and weight metadata

queries run against geospatial data and information discovered in distributed sources.

D 2003 Elsevier B.V. All rights reserved.

Keywords: Information utility; Multiple attribute utility; Multi-attribute utility functions; Geographic information systems

1. Introduction particular time of day or attending an appointment

Geospatial data are data having geographic and

spatial orientations: data having some information

content that includes a location component. Often,

geospatial data and information also have a temporal

aspect, an example of which is change detection over

time. The term ‘spatio-temporal’ pertains to these

types of data. People make ‘place’ and ‘time’ deci-

sions everyday: choosing the best route to the neigh-

borhood grocery store given the traffic patterns at a

236/$ - see front matter D 2003 Elsevier B.V. All rights reserved.

1016/S0167-9236(03)00076-9

orresponding author. Tel.: +1-703-251-7491; fax: +1-703-

04.

ail addresses: [email protected] (W.L. Meeks),

[email protected] (S. Dasgupta).

with your dentist are two simplistic examples [31]. It

has been estimated that nearly 80% of all data has a

location component [9]. Geospatial data are collected

and analyzed by organizations in order to solve a

broad array of public and private sector problems.

For example, epidemiologists have long studied the

case of Dr. John Snow who traced the 1854 outbreak of

cholera in London’s Soho district to a public water

pump on Broad Street by annotating on a street map

the home addresses or work locations of the sick and

dying. By correlating the locations and numbers of

cholera-infected patients, even though they were of

different social classes (and hence, different seemingly

unconnected lifestyles), Dr. Snow isolated the source

of the outbreak: the offending Broad Street water

W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–6348

pump. After a test of the water revealed bacterium

responsible, he was able to convince skeptical offi-

cials to remove the pump’s handle so no one could

draw contaminated water from the pump. The results

were dramatic and immediate; the number of

cholera cases quickly dropped off [51].

Today, the use of spatio-temporal data and infor-

mation greatly improves decision-making in many

different fields [16]. Examples in government in-

clude health and safety, public works, recreation and

culture, land use and zoning, administration, finan-

cial operations, other management, and military and

intelligence support. Examples in business include

marketing support and planning, retailing, various

types of services, wholesaling and distribution, and

other broad technical planning and support in a wide

variety of industries [1]. Thus, spatio-temporal anal-

yses are now becoming part of rigorous analytical

frameworks for local, national, and global security

and business intelligence realms. When using spatial

and temporal information to improve decision mak-

ing, attention must be paid to uncertainty and sensi-

tivity issues [21]. Attention must also be paid to

spatial and temporal scales relevant to the decision

being supported [45] and to the quality and utility of

available data, with respect to the intended use(s) of

the data [42]. This last issue defines the core

problem geospatial information utility (GeoIU)

addresses: that decision makers collect and use geo-

spatial data of varying spatial and temporal scales

in order to improve decision making, but more

attention needs to be paid to finding appropriate

Fig. 1. A simplified proces

methods for assessing the utility of the geospatial

data being used [10].

Beyond the data issues are system ones. Geospatial

data are collected by various means, processed, and

stored, or disseminated for later use by geographic

information systems (GIS), which require geospatial

data for spatial or spatio-temporal analysis and pre-

sentation. GIS are considered to include data, hard-

ware, software, procedures, operators (i.e., analysts),

and analytical requirements (i.e., the problem needing

to be solved). GIS exist primarily to ingest, manipu-

late, model, analyze, display, and output value-added

geospatial data, information, and products in multiple

file formats for some information (e.g., display) or

decision-making (i.e., through analysis) purposes

[12,28,33]. Fig. 1 depicts a simplified process model

for GIS.

Burrough [13] provides several definitions of GIS

by focusing on different perspectives; a tool-box view,

a database view, and organization-based view. These

views comport nicely with a broad 2001 summary of

information systems research conducted by Orlikow-

ski and Iacono [43]: they report on tool, proxy,

ensemble, and computational views of information

technology. Burrough’s three views identify some of

the complexity of the evolving geospatial sciences

domain: (1) GIS provide tools for analysts and deci-

sion makers to access and analyze complex, tempo-

rally, and spatially oriented data in order to improve

decision making; (2) GIS operate on spatially oriented

data, yet geospatial data and spatial databases are

complex; and (3) organizations use GIS to improve

s model for GIS use.

W.L. Meeks, S. Dasgupta / Decision Support Systems 38 (2004) 47–63 49

product and/or service offerings, and to improve

decision making by incorporating factors in ways

not previously considered.

Following data and systems’ issues come issues

surrounding the data providers. Geospatial data and

information are produced by a wide variety of gov-

ernmental and commercial producers and vendors. For

several years, a niche geospatial community of pro-

viders and users has been dealing with the evolving

complexity of the use and analysis of geospatial data.

Only now are geospatial data and spatio-temporal

analyses migrating beyond domain- or industry-spe-

cific niches. Decision makers in many fields, indus-

tries, and organizations are now coming to grips with

the benefits and challenges inherent in the storage,

retrieval, transmission, and analysis of these types of

data. They are also becoming aware of the breadth of

analyses that are possible when information-rich geo-

spatial and spatio-temporal data are made available

[8,24,34]. A community of interest that has sprung up

to integrate advances in GIS and geospatial data into

matters pertaining to US-focused homeland security,

called Homeland Infrastructure Foundation Level Da-

tabase (HIFLD), is aggressively pushing the bound-

aries on issues related to GIS and geospatial data. At a

recent HIFLD conference (Nov 2002), the most im-

portant issues pertaining to the use of geospatial data

were identified to be: data type (i.e., form of the data),

data schema or structure, metadata (i.e., presence,

quality, completeness), pedigree (i.e., source), quality

(i.e., pertains to content, horizontal, and vertical

accuracy), and currency (i.e., age relative to use) [5].

Conferences and communities of interest such as these

encourage the convergence of data, GIS, providers,

and users.

For purposes of this paper, it is useful to broadly

classify GIS and geospatial data users into two broad

categories: (1) governmental users and (2) non-gov-

ernmental users. Governmental users are primarily

interested in public domain uses of geospatial and

spatio-temporal data: for example, military planners

may require highly accurate, very current digital data

sets for planning flight routes for cruise missiles.

Flight route planning requires digital elevation mod-

els to support terrain contour matching algorithms

within the missiles’ guidance modules. To optimally

employ so-called ‘‘smart weapons’’ such as these,

planners and targeters must have access to current,

high-quality digital data sets with minimal horizontal

and vertical accuracy errors. In order to reduce

operational risk in the development of missile flight

routes based on new digital geospatial data sets, the

‘pedigree’ or quality of the supporting data must be

assessed [34]. As mentioned previously, many other

public sector uses of GIS and geospatial data abound:

fuel modeling to predict and prevent wildfires, ca-

dastral records and land tax planning, information

visualization of municipal government services via

Web-based GIS applications, and many more. Each

of these uses of geospatial data has different require-

ments for data accuracy, currency, and form; some

applications have stringent requirements and others

less so.

Non-governmental users are primarily interested in

commercial or business intelligence analyses of geo-

spatial data and information. These users may also

have varying needs for highly accurate and current

data sets: for example, planners in cell phone compa-

nies may employ GIS technologies and data analyses

in order to determine optimal site locations of a new

array of digital wireless telephone signal towers. Their

analyses might focus on making maximum use of

both send and receive signal strengths vis-a-vis local

terrain limitations (e.g., received signal strength is a

function of transmitted power, number and locations

of transmitter towers, radio frequency line-of-sight

obstacles, etc.) in order to minimize the number of

towers needed while providing a guaranteed quality of

service for cell phone subscribers; using fewer, well

placed towers may mean lower operating costs and

higher operating margins. Similar to the missile flight

route problem, a wireless telephone tower location

analysis based on geospatial data of poor or uncertain

quality is subject to errors, which may roll through the

calculations, quite possibly resulting in improperly

located towers, reduced systems performance, higher

installation and operations and maintenance costs, and

unhappy customers.

While assessing the value of geospatial informa-

tion content is now attracting the interest of research-

ers, current theories and approaches for valuing

information concentrate on the estimation of the

content value of textual information [7]. For example,

popular search engines use several different evalua-

tion schemes such as keyword proximity, keyword

density, and synonym matching, among others, to


estimate the quality of links and files returned from

Internet text searches. However, no such algorithmic

approaches exist for users, producers, and brokers of

geospatial information to estimate the value of the

geospatial information relative to the needs and uses

of geospatial users, producers, and brokers, nor to

dynamically perform these analyses rapidly, or in an

automated way. On the surface, text-oriented search,

retrieval, and valuation tools seem to have little use-

fulness and no analog in the geospatial domain. The

geospatial equivalent of a textual keyword or key

concept could be any spatially oriented feature com-

monly found within the geospatial data. Examples

include a road network or a single road, a bridge, a

stream, river, or other linear or area geographic feature

leading researchers to consider if the key ‘things’ to be

searched for are so different, what mechanisms must be

found or developed for performing search and retrieval

operations for geospatial data and information?

Considering both governmental and non-govern-

mental uses of geospatial data, the quality of the uses,

analyses, or decisions made based on available geo-

spatial data depends on the quality of the underlying

data being used. This paper outlines an algorithmic

approach to estimating the content value of geospatial

information and data sets for researchers and users of

this type of information.

Within the US government, the National Imagery

and Mapping Agency (NIMA) and the US Geological

Survey (USGS) are responsible for setting standards

and guiding the geospatial community (which in-

cludes all governmental and non-governmental enti-

ties) focused on government-related uses of geospatial

data [34]. The predecessor organization of NIMA, the

Defense Mapping Agency, had a long-established,

periodic product evaluation schema for evaluating

then-current products (i.e., geospatial data is provided

in many hard- (e.g., maps and charts) and soft-copy

forms (e.g., digital elevation models, raster map dis-

plays, vector map data sets)) based on published

product specifications. This approach to determining

product adequacy through performing product evalu-

ations from a producer’s point of view was flawed and

is no longer being used as official policy [42].

To determine the adequacy, relevance, and useful-

ness—herein defined as the ‘‘utility’’—of geospatial

information provided to consumers of the same within

the US government, geospatial products (i.e., both

hard maps and digital data sets) are presently assessed

as adequate in an adequate/not adequate binary ‘‘vac-

uum’’. That is, the geospatial information either meets

all predefined product adequacy standards and spec-

ifications for all content components, or it does not. If

it does not, then the intended consumers of these data

sets are not allowed access to them. This de facto

binary quality process is more about control than

access to data, and presumes:

(1) Information not meeting some predefined standard

has no value;

(2) All consumers of geospatial data essentially have

the same quality needs for all situations;

(3) Collectively, the producers of geospatial informa-

tion possess the same knowledge and awareness

of the consumers’ geospatial data and information

needs as the customers themselves and can

therefore evaluate the value of the geospatial

information provided to them [40].

As a result of this situation, two members of the

Veridian Corporation (including one of the authors)

were tasked under the auspices of NIMA’s Geospatial

Information Infrastructure Implementation Integrated

Process Team (GI3 IPT) to hypothesize an alternative

to the legacy product evaluation schema. The govern-

ment’s interest was two tracked: (1) perform a rapid

analysis of the theory needed to develop a proof-of-

concept prototype tool to automate geospatial infor-

mation utility (GeoIU) assessments, and (2) develop

the prototype. The work for the government was

performed during February to June 2001. This re-

search continues the theoretical analysis.

The robustness of the information age, including

activities such as mass customization of products and

information, is replacing mass production and mass

media as the preferred business value paradigms.

Government is learning from the business community

the lesson that one size does not fit all: that everything

is susceptible to change over time. The same is true of

geospatial information needs: as geospatial information

producers adopt a stronger customer orientation, they

must re-think how to assess the usefulness of the

geospatial information they provide to their customers.

In this paper, we propose a new metric called Geo-

spatial Information Utility (GeoIU), which estimates

the relevance of geospatial information to users. An

Fig. 2. A model building construct.


important point is that performing a utility assessment

based on the relevance of a select data set from a user’s

point of view, for any of n-specific intended uses at the

instant of assessment, is not the same as performing an

accuracy assessment. The literature is replete with

processes and cases for performing accuracy assess-

ments and computing error matrices on geospatial data

sets. Whereas, accuracy assessments measure statisti-

cal ‘‘goodness’’ against an absolute standard, such as

for a product specification [2,3,18,19,35,38,39,41],

utility assessments measure usefulness relative to the

user’s intended use [40].

GeoIU does not use a binary metric for adequacy;

instead, it provides users and producers with real-time,

interval information about the usefulness of available

data sets in addressing specific information needs.

GeoIU is responsive to the users’ intended uses because

it provides users the ability to weigh specific attribute

and utility measures based on their specific intended

uses, and it supports mechanisms for users and pro-

ducers to prioritize geospatial data acquisition and

production. More importantly, it directly addresses

the three assumptions listed above:

(1) Users of both hard- and soft-copy geospatial data

and products frequently comment that something

is better than nothing; therefore, geospatial

information producers should routinely allow

access to users of the best geospatial information

available at any given time, irrespective of its

estimated adequacy or its status with respect to

being finished data or work-in-process data.

(2) It is incorrect to assume that all users are the same,

or that they have the same information and

information quality needs. Therefore, it is also

folly to assume all intended uses, such as military

mission planning or installation planning for wire-

less telephone signal towers, have the same data

needs.

(3) As with most other products, physical or informa-

tional, the users of a product or service are the

ones best able to determine its utility versus its

intended use.

This paper examines information retrieval with

respect to determining information relevance. Then

it proposes a model to perform information utility

calculations using an information relevance paradigm

based on prior exploratory work performed by the UK

Defense Imagery and Geospatial Liaison Staff to the

US National Imagery and Mapping Agency [39]. In

the following sections, we provide a theoretical frame-

work based on a review of available literature related

to this concept, we provide a vision of how GeoIU

might be used to aid decision makers using geospatial

data and information and how it would be constructed,

we report some preliminary results, and finally we

provide implications for further research. Excellent

GIS and geospatial sciences glossaries can be found at

Refs. [2,3,13,41].

2. Envisioning GeoIU

This research is a model building effort based on

the following construct (Fig. 2), modified from Daa-

len, Thissen, and Verbraeck [23].

Obermeier [42] explicitly defined the goals and

functions needed in a model such as GeoIU; however,

as discussed above, other goals and functions have

been derived from the need for this sort of dynamic,

real-time service to be provided to GIS users to

improve their understandings about the limitations

of the analyses they perform. Outside of, but in


complement to, the processes of GIS, a program of

research into geospatial information utility model

building must address three sequential concepts: (1)

information retrieval, (2) information grading or val-

uation, and (3) information presentation.

As shown in Fig. 3 this paper is primarily

concerned with the information valuation or grading

aspect—the aspect most critical to decision sciences.

The study does this through examining multi-criteria

decision-making (MCDM) [46] and multiple attribute

utility theory (MAUT) [14]. The first and last con-

cepts: information retrieval and information presenta-

tion complement GeoIU model building, thus they are

considered, but only to the degree that they affect

GeoIU research. However, these concepts that remain

interrelated must be considered together when hypoth-

esizing and articulating a logical approach to address-

ing information relevance for geospatial information,

and for developing an algorithm capable of computing

and displaying the utility of geospatial information

within selectable parameters based on users’ informa-

tion needs. Finally, given the dearth of information

valuation research in the geospatial domain, many of

these concepts are examined in broader, non-geo-

spatial contexts as we ponder their specific applica-

bility to geospatial data and information. This paper

assumes that a conceptual correlation exists between

concepts, as they are being applied in the textual

Fig. 3. Assisting the GIS process with utili

domain, to the problem of determining information

utility for geospatial data.

As shown in Fig. 4, a top-level information utility

schematic, GeoIU is comprised of metrics about the

quality of available geospatial information and metrics

about the user’s intended uses for the information.

Considering in Fig. 1 the process model GIS use, the

first issue for a GeoIU ‘‘tool’’ is the information

retrieval problem.

3. Information retrieval

Information retrieval is about information discov-

ery and delivery, that is, discovering the right data sets

and bringing them to the analyst or application that

needs them for some user-defined purpose. Within

this model, the information quality aspect can be

further described as shown in Fig. 4. The key compo-

nents of information quality—relative to the users’

needs—are based on metadata queries, which score

metadata values found in selected, distributed geo-

spatial data sets and query scoring functions.

In this era of ‘‘information explosion’’, providing

the right information to the right person within a

reasonable amount of time is a very important goal

for today’s information retrieval systems [7,15]. Due

to the characteristics of different retrieval methods,

ty assessments via the GeoIU model.

Fig. 4. Basic functional structure for geospatial information utility.


conventional information retrieval models often suffer

from inaccurate and incomplete queries as well as

inconsistent content relevance. We agree and further

believe that users will have their own individual

intended interpretation of the semantic meanings of

their query terms, and that these meanings are not

always captured via search mechanism interfaces.

Chen and Kuo’s [15] discussion highlights problems

users of geospatial information utility will face: that is

rarely complete or homogeneous data (or metadata) of

uniform quality available. This is true even where

complex and well-defined metadata structures exist.

Horng and Yeh [29] propose a novel approach to

automatically retrieve keywords and then to use

generic algorithms to adapt keyword weights. The

core of the GeoIU algorithm rests on capturing and

applying user-defined search parameters, which are

based on intended use of the data returned from the

search, and which are compared in a relative ranking

fashion to determine appropriate weights. While tex-

tual retrieval and scoring models, such as Horng and

Yeho’s, rely on keyword weights, which help in the

examination—the scoring and weighting—of the tex-

tual data itself, the proposed GeoIU algorithm scores

retrieved geospatial metadata values as the de facto

keywords of a geospatial data search, in lieu of textual

keywords. The GeoIUmodel allows for weighing them

similarly, based on a user’s intended use of the geo-

spatial data discovered through the resultant search

query. The ability to develop appropriate user-defined

weighing factors contributes directly to increasing

customization of the GeoIU model.

Classical information retrieval theory uses the lan-

guage of documentation sets for the results of queries

[7,54]. Applied to the GIS problem, a document set

can be thought of as a consistent group or series of

maps, drawings, or records that have the same subject

matter, format, or purpose [41]. Information retrieval

strategies should be insensitive to modest changes in

the relevant document set since individual relevance

assessments are known to vary widely [54]. Investi-

gated are net change relevance assessments relative to

the evaluation of retrieval results. Very high correla-

tions were found among rankings of systems produced

using different relevance judgment sets. We consid-

ered this approach in the development of the GeoIU

model, but extend the Voorhees approach [54] because

it limits the authors’ concept for information utility as

their model is intentionally capable of detecting and

reporting on longitudinal (i.e., over time) changes in

GeoIU scores based on underlying changes in the

source data and metadata.

The structure and organization of geospatial infor-

mation utility queries and calculations should be

extended to an Internet-based search, discovery, and

retrieval paradigm in order to gain access to more and

richer data sources over time. Web or intranet search-

ing is shifting from presenting a list of ‘‘hits’’ to a

richer, more complex presentation of information,

delivering information in a meaningful context [6].

According to Sherman [47], ‘‘searching is a modern

technological wonder, but algorithms can only do

algorithmic functions. Humans perform other types

of functions, including putting information into con-

text and drawing relationships’’. We posit the future of

searching for geospatial information will be best

achieved by combining routine indexing searches with

putting rich human needs back into the search process.

And, as applied to this research, to clearly accommo-

date intended use(s) of discovered data as part of both

retrieval and evaluation functions.

Studies have been performed by analyzing over 1

million Web queries by users of the Excite search

Excite search engine. The Spink, et al. [49] study found

that most people use few search terms, few modified

their queries, view their retrievedWeb pages, and rarely

use advanced search features. This study provides

insight into public practices and choices inWeb search-

ing. The work of Spink et al. [49] has particular

relevance to the authors’ study as the prototype GeoIU

tool accounts for both ‘‘basic’’ and ‘‘power’’ users.

Implications about the sophistication of users are useful

in considering the breadth and complexity of search

and grading parameter inputs. Statistical association


measures have been widely used in information re-

trieval research [17]. Usually, these are based on

clustering documents and terms on the basis of their

relationships. Applications of association measures for

term clustering include automatic thesaurus construc-

tion and query expansion. Further, Jansen and Pooch

[32] discuss the current state of research on Web

searching. Considering this research to be at an incip-

ient stage, they hypothesize this to provide a unique

opportunity to review the state of research in the field,

identify common trends, develop a methodological

framework, and define terminology for future Web

searching studies. Importantly, they propose a frame-

work and implications for the design of studies into

Web information retrieval systems, which are useful in

focusing the GeoIU model, particularly with regards to

interfaces.

4. Information valuation

Methodologies must account for the shortfalls in

available data. As described in this paper, the users of

GeoIU must be able to: (1) define appropriate query

parameters, and (2) weigh the scoring protocols pro-

vided within the information quality segment of the

methodology as shown here.

After the retrieval problem is solved (or at least

addressed), the scoring of retrieved metadata values

must be accomplished. Given the dearth of direct

literature focused on valuing geospatial information,

we looked at two key topics: MCDM, characterized

by Saaty [46], Teng et al. [52], and Vincke [53], who

all discuss similar approaches to addressing and

solving multi-criteria decisions and to developing

multiple attribute utility models. Malczewski [38]

provides excellent treatment of the topic with respect

to GIS and spatio-temporal supported decisions. We

used these concepts both to clarify the domain (i.e., a

content issue) and to clarify our approach to applying

Multicriteria Decision Analysis to the framework of

GeoIU algorithm development. A related topic is

Butler’s MAUT [14]. We relied most heavily on

MAUT for our model development.

Fraser and Gluck [27] state that little is known

about how users employ metadata to evaluate the

relevance of geospatial information objects in satisfy-

ing the users’ information and decision making needs,

and that this might differ from non-geospatial infor-

mation needs. Metadata is normally defined as data

about data. In the United States today, geospatial

metadata structures are nominally guided by metadata

standards being developed by a multi-agency task

force called the Federal Geographic Data Committee

(FGDC) [4]. Similar standards are evolving interna-

tionally [30]. However, though these standards are

becoming more universally accepted, there remains

great variability in their application. For example,

even when well-defined metadata structures exist,

the values necessary to populate metadata tables are

often left null. An interesting and necessary outgrowth

of geospatial information utility research should be a

reevaluation of existing metadata structures in order to

link future metadata structures with different ways to

query and analyze metadata.

Marsden [39] provides a very useful model for

determining geospatial information utility. This model

considers the Fraser and Gluck [27] requirements for

robust and available geospatial metadata without

describing how that metadata is to be made available.

This model can be summarized as: information utility

is a function of the quality of the information made

available in any given query and of a user’s informa-

tion search needs, as related in user-defined functional

terms. The term ‘‘intended use’’ is used to describe the

end purpose or how geospatial information is com-

monly used. The notion of intended use provides an

insightful framework for describing use cases around

which user profiles and parametric preferences can be

developed.

Learning the users’ interest categories in a dynamic

environment, such as the Web, is challenging because

they (i.e., users’ interests) change over time. Novel

schemes to represent the users’ interest categories,

using adaptive algorithms to discover the dynamics of

users’ interest through the use of positive and negative

relevance feedback, have been developed [55]. Em-

pirical studies confirm the effectiveness of this sche-

ma to accurately model a user’s interest and to adapt

appropriately to various levels of changes in the user’s

interests. The notion of accommodating change in the

users’ interests over time is relevant and has been

considered in the GeoIU model and prototype tool

development. Spink and Greisdorf [48] hypothesize

that though the current dichotomous approach to

information relevance has produced abundant infor-


mation retrieval research, relevance studies that in-

clude consideration of the users’ relevance judgments

will provide greater clarity and congruity.

5. Information presentation

The data fusion issue is not trivial, but it is becoming

better understood over time [8,20,34,36,37]. Just as

there is routine data fusion with geospatial data content,

so too must an information utility algorithm be able to

fuse geospatial metadata. For example, a user’s query

for all road network data within a given scalar band,

within a selected area of interest (AOI), may yield

many different data holdings within several distributed

governmental, academic, foreign, and commercial

databases. If successfully accessed, there may be sev-

eral areas of overlap. These areas must be fused and

evaluated within the context of a new ‘‘whole’’. Dunlop

[25] presents the main lines of discussion about Mira, a

working group of the Commission of the European

Union Information Technologies Program, designed to

advance research in the area of evaluation frameworks

for interactive and multimedia information retrieval.

The Mira working group brought together many of the

leading researchers in information retrieval and hu-

man–computer interaction. Dunlop identifies the need

for more varied forms of evaluation, which need to be

considered to complement engine evaluation.

Stratigos and Curle [50] examined some common-

ly held ideas about end-users, specifically their skills

and abilities, and their understanding of the proper use

of the Internet and commercial information products.

They undertook this study because many assumptions

have been made about end-users and their informa-

tion-seeking skills. This study presents survey results,

one of the most important of which is discovering that

behaviors for verifying information generally have not

changed. The common portrayal of end-users as

uncritical consumers of information is not very accu-

rate. The study shows that users are not lazy infor-

mation seekers who accept at face value the first piece

of information that comes along; rather, users, in

general, are not likely to trust unverified information.

The Stratigos and Curle assumptions and results are

relevant to our work because we presume: (1) users

want to assert some control over their (geospatial)

information search and manipulation operations and,

(2) users are sophisticated enough to use some or all

of the ‘‘mass customization’’ features we envision for

them when the GeoIU model is operationalized as a

systematic application. Coincidentally, since some or

all of the data may need to be purchased, the infor-

mation utility query must aid the user in determining

whether or not to make the purchase.

The importance of the current literature lies not in

its alignment with this specifically focused topic,

because there are a few direct links between the

literature and the information relevance problem for

producers and users of geospatial information. Rather,

the importance of the literature lies in its ability to

point to important trends and ideas for further inves-

tigation in closely related disciplines. These pointers

have provided focus for the exploratory construct, and

have contributed to both the iterative development of

the algorithmic methodology and of a prototype tool

itself.

6. Computing GeoIU

The focus of this research is to determine a user-

oriented methodology for estimating the relevance

of geospatial information to users within existing

and future uses of these data and information. The

proposed GeoIU metric measures the usefulness of

geospatial information in terms of its spatial accu-

racy (accuracy is typically comprised of both spatial

accuracy (i.e., of the objects features contained in a

database) and content accuracy (i.e., of the attrib-

utes associated with each feature); in this context,

spatial accuracy is the measure of merit); currency;

percentage of area coverage; ‘‘usability’’ as deter-

mined by user-defined data types or forms; and

availability for use within a required datum. This

research attacks the core of the information utility

problem, which is the requirement to access select-

ed metadata records associated with specific geo-

spatial information data sets over any selected area

of interest (AOI), and integrate and analyze those

metadata in such a way as to create a utility

function for the underlying information set or

holding.

Fig. 7 provides the current GeoIU model within

an architectural schema as it applies Saaty’s [46],

Buede’s [11], and Butler et al. [14] multiple attribute

Fig. 5. Considering evaluative functions with respect to information quality.


utility models to the GeoIU problem. The evolution of

the models shown in Figs. 4–6 has lead to the

adaptation of the Marsden model, shown in Fig. 7.

Eastman et al. [26] address the need and a partial

approach, using raster techniques, for applying a

multi-criteria/multi-objective approach to geospatial

decision making. A prototype GeoIU desktop appli-

cation was developed using the model in Fig. 7 in

order to provide a proof of concept under contract to

NIMA [42]. The GeoIU tool searches the metadata of

targeted test datasets to examine and compare record-

level values for each geospatial data set or product

against normative function curves. The metadata val-

ue retrieved from a user’s GeoIU query provides a

function-derived score. This score represents a the-

matic-level score for one data query record within a

user-defined area of interest (AOI). Following the

Fig. 6. Considering evaluative functions

successful retrieval of one or more geospatial data

sets, two things can occur:

(1) the geospatial content must be manipulated within

the GIS.

(2) The geospatial metadata may or may not also be

accessed for separate evaluation and manipula-

tion.

Irrespective of the power and functionalities found

within a user’s particular GIS, toolset, or application

suite, where more than one data set has been

retrieved, quite frequently, the user is able to employ

the GIS application to fuse together various compo-

nents of the data set or multiple data sets, which

often contain thematic layers. Over time, more robust

GIS applications are being developed, including

with respect to information needs.

Fig. 7. Architectural schematic of the geospatial information utility function.


those for use within common Web browsers. For

example, a popular GIS tool such as Environmental

Sciences Research Institute’s (ESRI) ArcView GIS is

able to ingest geospatial data of different types,

including digital elevation data, road network cover-

age data and hydrographic, and fuse these data

together within the desktop application for the pur-

pose of performing a detailed terrain analysis such as

a cross-country mobility study based on soil compo-

sition and moisture content, slope percentage derived

from a detail elevation model or source, and vege-

tation density [44].

Computing GeoIU is a function of a real-time

analysis of available metadata for selected data sets


within targeted areas of interest. Besides being

algorithmically driven, which implies repeatable

consistency, information utility assessments could

easily be performed via a simple desktop tool or

application. This study hypothesizes that GeoIU may

be based on something as simple as a weighed-average

calculation using the sum of the weights for each

evaluation criteria (e.g., accuracy, currency, coverage,

usability form, and datum availability) applied to

factor scores for each criterion. The weight given to

each criterion would be user-defined. These calcu-

lations would be performed within use-oriented

scalar bands already familiar to geospatial consum-

ers. These bands of scalar representations (i.e.,

symbolizations) are important because they repre-

sent different levels of data density and feature

symbolization that users require to perform various

geospatial information analyses. Each evaluation

criterion will have comparative utility score values

found in community-defined look-up tables or func-

tion curves. Calculating GeoIU internal to the

GeoIU tool leads to a raw score for each criterion,

normalized for comparison and presentation. Nom-

inally, the computed IU score of an evaluated data

Fig. 8. Illustrating the effects of

set, within any intended use scale band, for exam-

ple, could then be summarized as the weighted-

average sum of all criteria scores:

GeoIUscore ¼X

GeoIUfactor scores

� individual criterion factorweights

Fig. 8 illustrates an example of the methodology

being evaluated in a proof-of-concept prototype tool

developed in early 2001. Only area coverage and

currency are illustrated here; however, this method-

ology of using look-up tables and continuous distri-

bution scoring functions must be based on parameters

(e.g., the shapes of the curves, including their coef-

ficients and exponents) agreed upon by the geospatial

information using community. These agreements

for developing scoring mechanisms suitable for add-

ressing critical metadata attributes (e.g., age of

available data in years or months for the currency

rating) must continue for all critical information

utility factors.

For an information utility assessment to be made

for a selected area of interest (e.g., the western

partial GeoIU calculations.


coast of equatorial Africa), the following steps

represent the sequence of implementation actions:

(1) A user uses an interactive graphical user interface

to select a specific area of interest at one or more

selected scale bands.

(2) The user then designates their own relative

attribute score weights as a means to establish

relative priorities, and then also selects any

minimum query thresholds for each of the utility

factors for their current intended use (note: the

sum of the weights must = 100%).

(3) The IU tool then queries metadata of available and

selected databases, linking to predetermined fields

for current data values relative to the IU assess-

ment (e.g., age of the data, spatial accuracy, etc.).

(4) As a result of each metadata query, individual

component utility factor scores are calculated.

(5) Each component utility factor score is then

multiplied by the previously defined utility factor

weights, providing individual criterion weighed

values.

(6) The individual weighed-values are then summed

together for computation of the information utility

assessment score for the selected area of interest.

(7) Where thematic ‘‘layer’’ information is available

(in either the data or metadata), the IU assessment

is preferred at the thematic level based on the

greater richness of the data at this level of detail.

When thematic-level IU scores can be calculated,

they are done and then aggregated into the final

IU score for the selected area of interest.

7. Model methodology

As depicted earlier, the basic GeoIU model focus-

es on two critical inputs: (1) information quality

based on the structure and results of metadata

queries, and (2) information needs based on the

users’ query-specific evaluative parameters. To an-

swer preliminary ‘Assess the Model’ questions, we

assumed a qualitative logic of inquiry oriented on

eliciting the most important data quality and intended

use stories from a limited user pool. To this end, we

began by parsing our small user pool of users by type

and interest. Divided into focus groups, called tech-

nical exchange meetings (TEMs) in government

parlance of 8–10 participants each, we held three

TEMs on GeoIU issues during May and June 2001.

In order to focus our TEM participants’ attention,

we developed an orientation of Constructive Empir-

icism [22] that we dubbed ‘‘anecdotal empiricism’’.

This term arose from the evolved structure of the

dialogue sessions with the TEM participants. The

term is meant to imply that we sought the TEM

participants’ experiences in their current geospatial

data use paradigms, through anecdotal storytelling.

This came to pass even though we attempted to

structure the TEM sessions through administration

of a two-page questionnaire focused on metadata

scoring and weighting issues.

Though we meant that there be statistical rigor to

this pilot data collection effort, as it turned out, the

users’ interest was so keen on discussing the core

GeoIU concepts and their enthusiasm for telling their

geospatial data use stories was so great that the

questionnaires served only as a vehicle for focusing

the TEM dialogues. Ultimately, they were not a

meaningful means to structure the users’ responses.

The questionnaire included questions about users’

data quality, currency, accuracy, form, and datum

issues. Of particular interest was uncovering effective

means to translate users’ stories as baseline data

points into a form useful in developing the scoring

functions for each of the principal areas of interest

(e.g., currency, accuracy, form).

As predicted, users based on three critical factors

of interest shown in Table 1.

These three critical factors of interest have been

empirically derived from interaction with participants

in the pilot TEMs. These factors can be thought of as

intersecting in a 4� 5 data cube that constitutes a

universe of possible user classes as shown in Fig. 9.

The part of the research dependent on characteriz-

ing and understanding users leads to scheduling the

user TEMs in order to present to them the core

concepts described and to perform a preliminary data

collection. The purpose of the data collection was

two-fold: (1) to gain insight into appropriate scoring

functions for each of the target metadata attributes

scored (e.g., age of the data, horizontal and vertical

accuracy, form of the data, the datum in which the

data are referenced, and area coverage related to the

selected AOI) and, (2) to gain insight into partici-

pants’ preferences for functions and interfaces. These

Table 1

Three factors of interest in characterizing government users classes

Sample scales of interest Sample functions

of interest

Broad

environments

of interest

Macro (e.g., < 1:500,000) Planning Land

Medium

(e.g., 1:25,000–1:500,000)

Operating Sea

Micro (e.g., >1:25,000) Modeling and

Simulation

Air

Detailed analyses Urban

(1) Scale is expressed as a ratio, where larger numbers represent

smaller scales and the rule of thumb is that large scales represent

small areas and vice versa. (2) The littoral zone occurs at the

meeting of sea and land, and it represents a special case for classes

of geospatial data and products. (3) TEMs are government parlance

for technical exchange meetings, which can be considered some-

what akin to a focus group with narrowly focused objectives.


pilot meetings were held in April and May 2001. A

total of 20 participants were interviewed in these

focus group-type meetings. Despite many TEM par-

ticipants’ unfamiliarity and some initial skepticism

with the core concepts associated with GeoIU and

the limited time available in these pilot meetings, the

first meetings provided tremendous insight into a

small population of users’ preferences, and some

opportunities for designing future TEMs on a much

larger scale.

A successful, albeit very limited prototype GeoIU

tool was developed as a Web-based desktop applica-

tion for NIMA during the writing of the 2001 Geo-

spatial Transition Plan [34]. This proof-of-concept

was tested on small ‘‘slices’’ of several different data

sets obtained from several different government-

owned databases. The types and quantities of records

Fig. 9. Considering relevant function

accessed represented a tiny fraction of types and

quantities available across the globe. However, this

approach did satisfy the proof-of-concept, ‘‘can we do

it?’’ question. As with most prototypes, the chief

value is as a communication vehicle between geo-

spatial information users and counterpart researchers,

systems developers, and other domain analysts. Re-

garding the ability to query metadata records for

associated geospatial content records, the prototype

development team encountered varying degrees of

success with the small number of different metadata

structures examined. This effort and the inspection of

various metadata attribute populations indicate that

significant follow-on effort needs to focus on meta-

data standards and attribute population.

8. Conclusion

The information utility metric being developed and

validated via this research is oriented on providing

decision makers who rely on the geospatial informa-

tion or other spatio-temporal data with a higher degree

of confidence in the quality of the underlying data

they employ in their decision support systems. Risk

can be managed or eliminated through greater insight

into the quality of the underlying data—from the

users’ intended use-based point of view, estimation

not currently available today.

9. Implications for researchers

This is an underserved field that has tremendous

potential based on the growing interest in spatio-

s in the user population cube.


temporal information. There exists both an opportu-

nity and a need for researchers to develop, test, and

refine models such as these. Just as practitioners have

the need for these types of tools in order to improve

the quality of their decision-making, researchers have

something to offer in terms of providing the analytical

rigor needed to improve the quality of these models.

There are both short- and long-term challenges for

those interested in furthering this work. In the short-

term, the models need refinement, primarily through

applying statistical confidence to the scoring func-

tions. Beyond that, a broader set of metadata attributes

should provide the basis for retrieval scoring and

weighting; age, accuracy, datum, and form of data

are not enough—though they served as useful for this

initial exploration—greater breadth and depth need to

be applied to the metadata attributes that are exam-

ined. In the longer term, work needs to be applied to

the metadata structures themselves. Defenders of the

current metadata structures argue that the metadata

standards and structures are sound and have been

vetted within the community.

10. Implications for managers

We posit this will be important and useful work for

managers because of the growing dependence on

geospatial information to aid managers of organiza-

tions in their decision-making activities. Envisioned

are five general use cases that the GeoIU model can

support:

o To provide longitudinal evaluations of changes in

available GI data and information by holding

constant one or more fixed query parameters

o To support multi-perspective (i.e., multiple user

class types) for organizational evaluations of

geospatial data over common areas of interest

o To support pre-acquiring evaluations of expensive

or difficult to acquire GI data

o To support focused or tailored geospatial analyses

o To support evaluations of the compatibility of GIS

tools and intended GI data sources

For both producers and users of geospatial data and

information, the litmus test will be whether the concept

and implementation of geospatial information utility

are sufficiently rigorous and useful to satisfy the users’

needs. To summarize: the proposed geospatial infor-

mation utility metric will measure the usefulness of

geospatial information found in multiple, distributed

libraries for individual users based on their missions,

tasks, and geospatial analysis problems.

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Lee Meeks is a PhD candidate at The George Washington University

in the Management Science Department of the School Of Business

and Public Management. His degrees include an MBA from George

Washington University and a BS from the US Naval Academy. He

works as a Senior Scientist and Program Manager within the

Geospatial Systems Group of Veridian Systems Division in Chan-

tilly, VA. He has spent over 7 years in the employ of or consulting to

the National Imagery and Mapping Agency. His current research

interest is improving organizational decision-making through the

innovative application of information technologies. His focus is

advances in geographical information systems (GIS) as a subset of

information systems, as GIS and spatio-temporal data are applied to

organizational decision-making. His previous publications have

been for the National Imagery and Mapping Agency. He has

presented at the Institute for Operations Research and Management

Science (INFORMS) annual meeting.

Subhasish Dasgupta is an assistant professor of information systems

in the School of Business and Public Management, The George

Washington University. He received his PhD from Baruch College,

The City University of New York (CUNY), and MBA and BS

degrees from the University of Calcutta, India. His current research

interests are electronic commerce, information technology adoption

and diffusion, and Internet-based simulations and games. He has

published in journals such as European Journal of Information

Systems, Logistics Information Management, Journal of Global

Information Management, Journal of Global Information Technol-

ogy Management, Simulation and Gaming Journal, and Electronic

Markets: The International Journal of Electronic Commerce and

Business Media. He has presented at numerous regional, national,

and international conferences.

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